A Bayesian stochastic SIRS model with a vaccination strategy for the analysis of respiratory syncytial virus

[EN] Our objective in this paper is to model the dynamics of respiratory syncytial virus in the region of Valencia (Spain) and analyse the effect of vaccination strategies from a health-economic point of view. Compartmental mathematical models based on differential equations are commonly used in epidemiology to both understand the underlying mechanisms that influence disease transmission and analyse the impact of vaccination programs. However, a recently proposed Bayesian stochastic susceptible-infected-recovered-susceptible model in discrete-time provided an improved and more natural description of disease dynamics. In this work, we propose an extension of that stochastic model that allows us to simulate and assess the effect of a vaccination strategy that consists on vaccinating a proportion of newborns.This work has been supported by Grant Number MTM2014-56233-P from the Spanish Ministry of Economy and Competitiveness.Jornet-Sanz, M.; Corberán-Vallet, A.; Santonja, F.; Villanueva Micó, RJ. (2017). A Bayesian stochastic SIRS model with a vaccination strategy for the analysis of respiratory syncytial virus. SORT. Statistics and Operations Research Transactions. 41(1):159-175. https://doi.org/10.2436/20.8080.02.56S15917541

Modeling Chickenpox Dynamics with a Discrete Time Bayesian Stochastic Compartmental Model

[EN] We present a Bayesian stochastic susceptible-exposed-infectious-recovered model in discrete time to understand chickenpox transmission in the Valencian Community, Spain. During the last decades, different strategies have been introduced in the routine immunization program in order to reduce the impact of this disease, which remains a public health's great concern. Under this scenario, a model capable of explaining closely the dynamics of chickenpox under the different vaccination strategies is of utter importance to assess their effectiveness. The proposed model takes into account both heterogeneous mixing of individuals in the population and the inherent stochasticity in the transmission of the disease. As shown in a comparative study, these assumptions are fundamental to describe properly the evolution of the disease. The Bayesian analysis of the model allows us to calculate the posterior distribution of the model parameters and the posterior predictive distribution of chickenpox incidence, which facilitates the computation of point forecasts and prediction intervals.This work has been supported by a research grant from the Spanish Ministry of Economy and Competitiveness (MTM2017-83850-P).Corberán-Vallet, A.; Santonja, F.; Jornet-Sanz, M.; Villanueva Micó, RJ. (2018). Modeling Chickenpox Dynamics with a Discrete Time Bayesian Stochastic Compartmental Model. Complexity. 1-9. https://doi.org/10.1155/2018/3060368S19Acedo, L., Moraño, J.-A., Santonja, F.-J., & Villanueva, R.-J. (2016). A deterministic model for highly contagious diseases: The case of varicella. Physica A: Statistical Mechanics and its Applications, 450, 278-286. doi:10.1016/j.physa.2015.12.153Díez-Gandía, A., Villanueva, R.-J., Moraño, J.-A., Acedo, L., Mollar, J., & Díez-Domingo, J. (2016). Studying the Herd Immunity Effect of the Varicella Vaccine in the Community of Valencia, Spain. Lecture Notes in Computer Science, 38-46. doi:10.1007/978-3-319-31744-1_4Stochastic epidemic models with a backward bifurcation. (2006). Mathematical Biosciences and Engineering, 3(3), 445-458. doi:10.3934/mbe.2006.3.445Roberts, M., Andreasen, V., Lloyd, A., & Pellis, L. (2015). Nine challenges for deterministic epidemic models. Epidemics, 10, 49-53. doi:10.1016/j.epidem.2014.09.006Corberán-Vallet, A., & Santonja, F. J. (2014). A Bayesian SIRS model for the analysis of respiratory syncytial virus in the region of Valencia, Spain. Biometrical Journal, 56(5), 808-818. doi:10.1002/bimj.201300194Bjørnstad, O. N., Finkenstädt, B. F., & Grenfell, B. T. (2002). DYNAMICS OF MEASLES EPIDEMICS: ESTIMATING SCALING OF TRANSMISSION RATES USING A TIME SERIES SIR MODEL. Ecological Monographs, 72(2), 169-184. doi:10.1890/0012-9615(2002)072[0169:domees]2.0.co;2Gelman, A. (2006). Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper). Bayesian Analysis, 1(3), 515-534. doi:10.1214/06-ba117aLunn, D., Spiegelhalter, D., Thomas, A., & Best, N. (2009). Rejoinder to commentaries on ‘The BUGS project: Evolution, critique and future directions’. Statistics in Medicine, 28(25), 3081-3082. doi:10.1002/sim.369

A new approach to portfolio selection based on forecasting

In this paper we analyze the portfolio selection problem from a novel perspective based on the analysis and prediction of the time series corresponding to the portfolio’s value. Namely, we define the value of a particular portfolio at the time of its acquisition. Using the time series of historical prices of the different financial assets, we calculate backward the value that said portfolio would have had in past time periods. A damped trend model is then used to analyze this time series and to predict the future values of the portfolio, providing estimates of the mean and variance for different forecasting horizons. These measures are used to formulate the portfolio selection problem, which is solved using a multi-objective genetic algorithm. To show the performance of this procedure, we use a data set of asset prices from the New York Stock Market

Innocampus Explora: Nuevas formas de comunicar ciencia

[EN] Innocampus Explora aims to show the students of the Burjassot-Paterna campus of the Universitat de València how the different scientific degrees are interrelated. To do this we propose activities in which students and teachers work together to cover the interdisciplinary nature of science, both in everyday and professional issues. Throughout this course the activities developed relate to new ways to communicate science. With the development of this project we contribute to a transversal quality education for all the participating students.[ES] Innocampus Explora tiene por objetivo mostrar a los estudiantes del campus de Burjassot-Paterna de la Universitat de València cómo los diferentes grados científicos están interrelacionados. Para ello proponemos actividades en las que estudiantes y profesores trabajen conjuntamente para abarcar la interdisciplinariedad de la ciencia, tanto en temas cotidianos como profesionales. A lo largo de este curso las actividades desarrolladas se relacionan con las nuevas formas de comunicar ciencia. Con el desarrollo de este proyecto contribuimos a una formación transversal de calidad para todos los estudiantes participantes.Moros Gregorio, J.; Rodrigo Martínez, P.; Torres Piedras, C.; Montoya Martínez, L.; Peña Peña, J.; Pla Díaz, M.; Galarza Jiménez, P.... (2019). Innocampus Explora: Nuevas formas de comunicar ciencia. En IN-RED 2019. V Congreso de Innovación Educativa y Docencia en Red. Editorial Universitat Politècnica de València. 814-823. https://doi.org/10.4995/INRED2019.2019.10449OCS81482

Forecasting correlated time series with exponential smoothing models

This paper presents the Bayesian analysis of a general multivariate exponential smoothing model that allows us to forecast time series jointly, subject to correlated random disturbances. The general multivariate model, which can be formulated as a seemingly unrelated regression model, includes the previously studied homogeneous multivariate Holt-Winters' model as a special case when all of the univariate series share a common structure. MCMC simulation techniques are required in order to approach the non-analytically tractable posterior distribution of the model parameters. The predictive distribution is then estimated using Monte Carlo integration. A Bayesian model selection criterion is introduced into the forecasting scheme for selecting the most adequate multivariate model for describing the behaviour of the time series under study. The forecasting performance of this procedure is tested using some real examples.Bayesian forecasting Exponential smoothing Innovations state space models Model selection Monte Carlo methods Multivariate time series

A Multivariate Age-Structured Stochastic Model with Immunization Strategies to Describe Bronchiolitis Dynamics

Bronchiolitis has a high morbidity in children under 2 years old. Respiratory syncytial virus (RSV) is the most common pathogen causing the disease. At present, there is only a costly humanized monoclonal RSV-specific antibody to prevent RSV. However, different immunization strategies are being developed. Hence, evaluation and comparison of their impact is important for policymakers. The analysis of the disease with a Bayesian stochastic compartmental model provided an improved and more natural description of its dynamics. However, the consideration of different age groups is still needed, since disease transmission greatly varies with age. In this work, we propose a multivariate age-structured stochastic model to understand bronchiolitis dynamics in children younger than 2 years of age considering high-quality data from the Valencia health system integrated database. Our modeling approach combines ideas from compartmental models and Bayesian hierarchical Poisson models in a novel way. Finally, we develop an extension of the model that simulates the effect of potential newborn immunization scenarios on the burden of disease. We provide an app tool that estimates the expected reduction in bronchiolitis episodes for a range of different values of uptake and effectiveness

Analysis of Weighting Strategies for Improving the Accuracy of Combined Forecasts

This paper deals with the weighted combination of forecasting methods using intelligent strategies for achieving accurate forecasts. In an effort to improve forecasting accuracy, we develop an algorithm that optimizes both the methods used in the combination and the weights assigned to the individual forecasts, COmbEB. The performance of our procedure can be enhanced by analyzing separately seasonal and non-seasonal time series. We study the relationships between prediction errors in the validation set and those of ex-post forecasts for different planning horizons. This study reveals the importance of setting the size of the validation set in a proper way. The performance of the proposed strategy is compared with that of the best prediction strategy in the analysis of each of the 100,000 series included in the M4 Competition

Analysis of Weighting Strategies for Improving the Accuracy of Combined Forecasts

This paper deals with the weighted combination of forecasting methods using intelligent strategies for achieving accurate forecasts. In an effort to improve forecasting accuracy, we develop an algorithm that optimizes both the methods used in the combination and the weights assigned to the individual forecasts, COmbEB. The performance of our procedure can be enhanced by analyzing separately seasonal and non-seasonal time series. We study the relationships between prediction errors in the validation set and those of ex-post forecasts for different planning horizons. This study reveals the importance of setting the size of the validation set in a proper way. The performance of the proposed strategy is compared with that of the best prediction strategy in the analysis of each of the 100,000 series included in the M4 Competition